-----------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  c:\data\jleoccp.log
  log type:  text
 opened on:  15 Dec 2010, 11:51:11

. *main results
. xtmelogit vote retdiff npartdiff partdiff retention npart djudge ijudge closepl closepc factp
> l catdum3 catdum4 catdum5 || stateid: || judgeid: 

Refining starting values: 

Iteration 0:   log likelihood = -534.00329  
Iteration 1:   log likelihood =  -523.6741  
Iteration 2:   log likelihood = -522.03801  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -522.03801  
Iteration 1:   log likelihood =  -522.0165  
Iteration 2:   log likelihood = -522.01644  

Mixed-effects logistic regression               Number of obs      =       818

--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
        stateid |       27          3       30.3        154           7
       judgeid2 |      295          1        2.8         15           7
--------------------------------------------------------------------------

                                                Wald chi2(13)      =     39.46
Log likelihood = -522.01644                     Prob > chi2        =    0.0002

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |    4.00234   2.021796     1.98   0.048      .039693    7.964987
   npartdiff |   5.012016   2.044839     2.45   0.014     1.004206    9.019825
    partdiff |  -1.480981   2.441453    -0.61   0.544    -6.266141    3.304179
   retention |   .0179744   1.087322     0.02   0.987    -2.113137    2.149086
       npart |   .0248843   1.084652     0.02   0.982    -2.100995    2.150763
      djudge |  -.3978119   .2097658    -1.90   0.058    -.8089452    .0133215
      ijudge |  -.6481533    .677854    -0.96   0.339    -1.976723    .6804163
     closepl |   .2598086   .1865085     1.39   0.164    -.1057413    .6253585
     closepc |   1.040151   .9121791     1.14   0.254    -.7476873    2.827989
      factpl |   .5935989   .2050433     2.89   0.004     .1917214    .9954764
     catdum3 |   .7422761   .3118759     2.38   0.017     .1310106    1.353542
     catdum4 |  -.3222656    .269805    -1.19   0.232    -.8510737    .2065424
     catdum5 |  -.5121978   .3607919    -1.42   0.156    -1.219337    .1949413
       _cons |  -.7700334   1.030099    -0.75   0.455    -2.788991    1.248924
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |    1.00258   .2664261      .5955532    1.687788
-----------------------------+------------------------------------------------
judgeid2: Identity           |
                   sd(_cons) |    .509306   .1505819      .2853065    .9091717
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =    24.60   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. test retdiff-partdiff=0

 ( 1)  [eq1]retdiff - [eq1]partdiff = 0

           chi2(  1) =    3.05
         Prob > chi2 =    0.0807

. test retdiff-npartdiff=0

 ( 1)  [eq1]retdiff - [eq1]npartdiff = 0

           chi2(  1) =    0.14
         Prob > chi2 =    0.7114

. test npartdiff-partdiff=0

 ( 1)  [eq1]npartdiff - [eq1]partdiff = 0

           chi2(  1) =    4.45
         Prob > chi2 =    0.0350

. margins, predict(fixedonly) dydx(*) atmeans

Conditional marginal effects                      Number of obs   =        818

Expression   : Predicted mean, fixed portion only, predict(fixedonly)
dy/dx w.r.t. : retdiff npartdiff partdiff retention npart djudge ijudge closepl closepc
               factpl catdum3 catdum4 catdum5
at           : retdiff         =    .0453937 (mean)
               npartdiff       =    .0491919 (mean)
               partdiff        =    .1321982 (mean)
               retention       =    .2775061 (mean)
               npart           =    .2909535 (mean)
               djudge          =     .502445 (mean)
               ijudge          =    .0207824 (mean)
               closepl         =    .2665037 (mean)
               closepc         =    .0146699 (mean)
               factpl          =    .6185819 (mean)
               catdum3         =    .2616137 (mean)
               catdum4         =    .3435208 (mean)
               catdum5         =    .1638142 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   .9801991   .4952762     1.98   0.048     .0094756    1.950923
   npartdiff |   1.227475   .5068889     2.42   0.015     .2339913    2.220959
    partdiff |  -.3627019   .6003204    -0.60   0.546    -1.539308    .8139045
   retention |    .004402   .2662333     0.02   0.987    -.5174057    .5262098
       npart |   .0060943   .2655541     0.02   0.982    -.5143822    .5265708
      djudge |  -.0974267   .0518954    -1.88   0.060    -.1991398    .0042863
      ijudge |   -.158737   .1662158    -0.96   0.340     -.484514    .1670401
     closepl |   .0636288   .0457467     1.39   0.164     -.026033    .1532906
     closepc |   .2547397   .2238974     1.14   0.255    -.1840911    .6935706
      factpl |   .1453762   .0503683     2.89   0.004     .0466562    .2440963
     catdum3 |   .1817883    .077138     2.36   0.018     .0306006    .3329759
     catdum4 |   -.078925   .0660758    -1.19   0.232    -.2084312    .0505813
     catdum5 |  -.1254406   .0886601    -1.41   0.157    -.2992112    .0483301
------------------------------------------------------------------------------

. xtmelogit vote retdiff npartdiff partdiff retention npart djudge ijudge closepl closepc catdu
> m2 catdum3 catdum4 catdum5 || stateid: || judgeid: 

Refining starting values: 

Iteration 0:   log likelihood = -805.21053  
Iteration 1:   log likelihood =  -788.1119  
Iteration 2:   log likelihood = -787.09705  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -787.09705  
Iteration 1:   log likelihood =  -787.0866  
Iteration 2:   log likelihood =  -787.0866  

Mixed-effects logistic regression               Number of obs      =      1233

--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
        stateid |       30          5       41.1        196           7
       judgeid2 |      390          1        3.2         20           7
--------------------------------------------------------------------------

                                                Wald chi2(13)      =     32.89
Log likelihood =  -787.0866                     Prob > chi2        =    0.0018

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   3.368913     1.4983     2.25   0.025     .4322984    6.305528
   npartdiff |   4.232615   1.587592     2.67   0.008     1.120992    7.344237
    partdiff |   1.443226   2.077316     0.69   0.487    -2.628239    5.514692
   retention |   .6899699   .8840741     0.78   0.435    -1.042784    2.422723
       npart |   .0414423   .9040878     0.05   0.963    -1.730537    1.813422
      djudge |  -.4237262   .1653487    -2.56   0.010    -.7478037   -.0996487
      ijudge |  -.3868038   .5239317    -0.74   0.460    -1.413691    .6400835
     closepl |   .1865284   .1466551     1.27   0.203    -.1009103    .4739671
     closepc |   .5277097   .5057612     1.04   0.297     -.463564    1.518984
     catdum2 |   .7199859   .2463474     2.92   0.003     .2371539    1.202818
     catdum3 |   .4169149   .2512923     1.66   0.097     -.075609    .9094388
     catdum4 |   .2563443   .2160327     1.19   0.235     -.167072    .6797605
     catdum5 |  -.0667675   .2601172    -0.26   0.797    -.5765879     .443053
       _cons |  -1.224191   .8672754    -1.41   0.158    -2.924019    .4756376
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   .8896696   .1874754      .5886533    1.344615
-----------------------------+------------------------------------------------
judgeid2: Identity           |
                   sd(_cons) |   .3866455   .1308998      .1991299    .7507398
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =    48.18   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. test retdiff-partdiff=0

 ( 1)  [eq1]retdiff - [eq1]partdiff = 0

           chi2(  1) =    0.59
         Prob > chi2 =    0.4440

. test retdiff-npartdiff=0

 ( 1)  [eq1]retdiff - [eq1]npartdiff = 0

           chi2(  1) =    0.18
         Prob > chi2 =    0.6740

. test npartdiff-partdiff=0

 ( 1)  [eq1]npartdiff - [eq1]partdiff = 0

           chi2(  1) =    1.30
         Prob > chi2 =    0.2551

. margins, predict(fixedonly) dydx(*) atmeans

Conditional marginal effects                      Number of obs   =       1233

Expression   : Predicted mean, fixed portion only, predict(fixedonly)
dy/dx w.r.t. : retdiff npartdiff partdiff retention npart djudge ijudge closepl closepc
               catdum2 catdum3 catdum4 catdum5
at           : retdiff         =    .0422704 (mean)
               npartdiff       =     .048278 (mean)
               partdiff        =    .1303035 (mean)
               retention       =    .2919708 (mean)
               npart           =    .2944039 (mean)
               djudge          =    .5336577 (mean)
               ijudge          =    .0202758 (mean)
               closepl         =    .2749392 (mean)
               closepc         =    .0218978 (mean)
               catdum2         =    .1646391 (mean)
               catdum3         =    .2270884 (mean)
               catdum4         =     .270073 (mean)
               catdum5         =    .1192214 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   .8136933   .3638769     2.24   0.025     .1005077    1.526879
   npartdiff |   1.022303   .3898174     2.62   0.009      .258275    1.786331
    partdiff |   .3485823   .5000848     0.70   0.486    -.6315658    1.328731
   retention |   .1666484   .2114653     0.79   0.431     -.247816    .5811127
       npart |   .0100095   .2182421     0.05   0.963    -.4177371    .4377562
      djudge |  -.1023425   .0404378    -2.53   0.011    -.1815991    -.023086
      ijudge |  -.0934247   .1266287    -0.74   0.461    -.3416124    .1547631
     closepl |   .0450522   .0354763     1.27   0.204      -.02448    .1145844
     closepc |   .1274577   .1222998     1.04   0.297    -.1122455    .3671609
     catdum2 |   .1738981   .0599997     2.90   0.004     .0563009    .2914954
     catdum3 |   .1006974   .0611749     1.65   0.100    -.0192032     .220598
     catdum4 |   .0619148   .0523219     1.18   0.237    -.0406342    .1644638
     catdum5 |  -.0161263   .0628597    -0.26   0.798     -.139329    .1070763
------------------------------------------------------------------------------

. *without the main effects, because of the collinearity; either include in main table or as ap
> pendix table
. xtmelogit vote retdiff npartdiff partdiff djudge ijudge closepl closepc factpl catdum3 catdum
> 4 catdum5 || stateid: || judgeid: 

Refining starting values: 

Iteration 0:   log likelihood = -534.26098  
Iteration 1:   log likelihood = -523.63345  
Iteration 2:   log likelihood = -522.03683  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -522.03683  
Iteration 1:   log likelihood = -522.01675  
Iteration 2:   log likelihood = -522.01671  

Mixed-effects logistic regression               Number of obs      =       818

--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
        stateid |       27          3       30.3        154           7
       judgeid2 |      295          1        2.8         15           7
--------------------------------------------------------------------------

                                                Wald chi2(11)      =     39.49
Log likelihood = -522.01671                     Prob > chi2        =    0.0000

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   3.998489    1.81095     2.21   0.027     .4490936    7.547885
   npartdiff |   5.027866   1.778161     2.83   0.005     1.542734    8.512998
    partdiff |  -1.521856   1.420678    -1.07   0.284    -4.306334    1.262622
      djudge |  -.3990563   .1994572    -2.00   0.045    -.7899853   -.0081273
      ijudge |  -.6485247   .6764953    -0.96   0.338    -1.974431    .6773818
     closepl |   .2597269   .1864352     1.39   0.164    -.1056793    .6251331
     closepc |   1.042484   .9057717     1.15   0.250    -.7327961    2.817764
      factpl |   .5933429    .204087     2.91   0.004     .1933398     .993346
     catdum3 |   .7426349   .3072194     2.42   0.016      .140496    1.344774
     catdum4 |  -.3226669   .2677031    -1.21   0.228    -.8473553    .2020215
     catdum5 |  -.5129426   .3545418    -1.45   0.148    -1.207832    .1819465
       _cons |  -.7499204   .3966253    -1.89   0.059    -1.527292    .0274509
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   1.001758   .2633044       .598454    1.676851
-----------------------------+------------------------------------------------
judgeid2: Identity           |
                   sd(_cons) |   .5093886   .1505253      .2854417    .9090358
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =    25.05   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. xtmelogit vote retdiff npartdiff partdiff djudge ijudge closepl closepc catdum2 catdum3 catdu
> m4 catdum5 || stateid: || judgeid: 

Refining starting values: 

Iteration 0:   log likelihood =  -805.9788  
Iteration 1:   log likelihood = -789.01547  
Iteration 2:   log likelihood = -787.91299  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -787.91299  
Iteration 1:   log likelihood = -787.90353  
Iteration 2:   log likelihood = -787.90353  

Mixed-effects logistic regression               Number of obs      =      1233

--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
        stateid |       30          5       41.1        196           7
       judgeid2 |      390          1        3.2         20           7
--------------------------------------------------------------------------

                                                Wald chi2(11)      =     31.18
Log likelihood = -787.90353                     Prob > chi2        =    0.0010

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   4.246644   1.403148     3.03   0.002     1.496524    6.996765
   npartdiff |   3.529575   1.423764     2.48   0.013     .7390479    6.320102
    partdiff |   .6546485   1.201725     0.54   0.586     -1.70069    3.009987
      djudge |  -.4456235   .1592209    -2.80   0.005    -.7576908   -.1335562
      ijudge |  -.4191144    .523453    -0.80   0.423    -1.445063    .6068346
     closepl |   .1831277   .1467068     1.25   0.212    -.1044124    .4706678
     closepc |   .5341039   .5049205     1.06   0.290     -.455522     1.52373
     catdum2 |   .6776347   .2451675     2.76   0.006     .1971152    1.158154
     catdum3 |   .4196776   .2506357     1.67   0.094    -.0715593    .9109146
     catdum4 |   .2318419   .2150124     1.08   0.281    -.1895746    .6532585
     catdum5 |  -.0895007   .2583857    -0.35   0.729    -.5959273    .4169259
       _cons |  -.8430584   .3256607    -2.59   0.010    -1.481342   -.2047752
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   .9449903   .2007728      .6231333    1.433091
-----------------------------+------------------------------------------------
judgeid2: Identity           |
                   sd(_cons) |    .385688   .1309051      .1983045    .7501356
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =    52.19   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. corr retdiff partdiff npartdiff diff retention part npart if vote!=. & judgepartynogovs!=. 
(obs=1176)

             |  retdiff partdiff npartd~f     diff retent~n     part    npart
-------------+---------------------------------------------------------------
     retdiff |   1.0000
    partdiff |  -0.2961   1.0000
   npartdiff |  -0.1711  -0.3932   1.0000
        diff |   0.1579   0.6745   0.1749   1.0000
   retention |   0.6121  -0.4838  -0.2796  -0.3783   1.0000
        part |  -0.3152   0.9393  -0.4186   0.5744  -0.5150   1.0000
       npart |  -0.2399  -0.5511   0.7133  -0.2591  -0.3919  -0.5868   1.0000


. *alternative way of dealing with the collinearity; for refs table
. xtmelogit congruence retention npart ptypop closenew factencourages catdum3 catdum4 catdum5 |
> | stateid: || judgeid: 

Refining starting values: 

Iteration 0:   log likelihood = -539.95835  
Iteration 1:   log likelihood =  -531.8077  
Iteration 2:   log likelihood = -529.13626  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -529.13626  
Iteration 1:   log likelihood = -528.90147  
Iteration 2:   log likelihood = -528.89885  
Iteration 3:   log likelihood = -528.89885  

Mixed-effects logistic regression               Number of obs      =       818

--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
        stateid |       27          3       30.3        154           7
       judgeid2 |      295          1        2.8         15           7
--------------------------------------------------------------------------

                                                Wald chi2(8)       =     30.10
Log likelihood = -528.89885                     Prob > chi2        =    0.0002

------------------------------------------------------------------------------
  congruence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   retention |   1.116981   .5511642     2.03   0.043     .0367195    2.197243
       npart |   1.301243   .5607611     2.32   0.020     .2021718    2.400315
      ptypop |   .2869276   .1922986     1.49   0.136    -.0899708     .663826
    closenew |   .2540202   .1795025     1.42   0.157    -.0977981    .6058385
factencour~e |   .6869917   .2034371     3.38   0.001     .2882623    1.085721
     catdum3 |   .7273724   .2944433     2.47   0.013     .1502741    1.304471
     catdum4 |  -.3267346    .262296    -1.25   0.213    -.8408253    .1873562
     catdum5 |  -.3343213   .3374695    -0.99   0.322    -.9957493    .3271066
       _cons |  -1.741111   .4837323    -3.60   0.000    -2.689208   -.7930128
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   .8008071   .2427099      .4421243    1.450479
-----------------------------+------------------------------------------------
judgeid2: Identity           |
                   sd(_cons) |   .4882787   .1492441      .2682223    .8888748
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =    18.65   Prob > chi2 = 0.0001

Note: LR test is conservative and provided only for reference.

. xtmelogit congruence retention npart ptypop closenew catdum2 catdum3 catdum4 catdum5 || state
> id: || judgeid: 

Refining starting values: 

Iteration 0:   log likelihood = -805.47563  
Iteration 1:   log likelihood =  -790.1259  
Iteration 2:   log likelihood = -789.31178  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -789.31178  
Iteration 1:   log likelihood = -789.30655  
Iteration 2:   log likelihood = -789.30655  

Mixed-effects logistic regression               Number of obs      =      1233

--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
        stateid |       30          5       41.1        196           7
       judgeid2 |      390          1        3.2         20           7
--------------------------------------------------------------------------

                                                Wald chi2(8)       =     17.29
Log likelihood = -789.30655                     Prob > chi2        =    0.0272

------------------------------------------------------------------------------
  congruence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   retention |   .9476686   .4890148     1.94   0.053    -.0107828     1.90612
       npart |   .6471724   .4680542     1.38   0.167     -.270197    1.564542
      ptypop |   .3980845   .1544749     2.58   0.010     .0953193    .7008497
    closenew |   .1297878   .1408026     0.92   0.357    -.1461802    .4057559
     catdum2 |   .2536231   .2382603     1.06   0.287    -.2133585    .7206046
     catdum3 |   .1514596   .2421878     0.63   0.532    -.3232197    .6261388
     catdum4 |  -.0389731   .2122062    -0.18   0.854    -.4548896    .3769434
     catdum5 |  -.3491951    .257474    -1.36   0.175    -.8538348    .1554447
       _cons |  -1.173535   .4276609    -2.74   0.006    -2.011735   -.3353354
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   .9690195   .1946341      .6536766    1.436488
-----------------------------+------------------------------------------------
judgeid2: Identity           |
                   sd(_cons) |   .3693019   .1309004      .1843614    .7397637
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =    64.68   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. *without the judgeid (for a footnote, or perhaps in conjunction with discussing the lack of a
> n effect for closepl closepc); for refs
. xtmelogit vote retdiff npartdiff partdiff retention npart djudge ijudge closepl closepc factp
> l catdum3 catdum4 catdum5 || stateid: 

Refining starting values: 

Iteration 0:   log likelihood = -531.91998  
Iteration 1:   log likelihood = -526.65694  
Iteration 2:   log likelihood = -524.46372  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -524.46372  
Iteration 1:   log likelihood = -524.42338  
Iteration 2:   log likelihood =  -524.4233  
Iteration 3:   log likelihood =  -524.4233  

Mixed-effects logistic regression               Number of obs      =       818
Group variable: stateid                         Number of groups   =        27

                                                Obs per group: min =         3
                                                               avg =      30.3
                                                               max =       154

Integration points =   7                        Wald chi2(13)      =     41.88
Log likelihood =  -524.4233                     Prob > chi2        =    0.0001

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   3.838167   1.936143     1.98   0.047     .0433964    7.632937
   npartdiff |   4.814949   1.940151     2.48   0.013     1.012322    8.617575
    partdiff |  -1.286485   2.290625    -0.56   0.574    -5.776027    3.203057
   retention |   .0692682   1.034359     0.07   0.947    -1.958038    2.096575
       npart |   .0754066   1.032725     0.07   0.942    -1.948698    2.099511
      djudge |  -.4042935   .1886843    -2.14   0.032    -.7741079    -.034479
      ijudge |    -.57791   .5924074    -0.98   0.329    -1.739007    .5831871
     closepl |   .2849203   .1762733     1.62   0.106    -.0605691    .6304097
     closepc |   .9561053   .8587085     1.11   0.266    -.7269325    2.639143
      factpl |   .5701548   .1979343     2.88   0.004     .1822107    .9580989
     catdum3 |   .6813679    .296473     2.30   0.022     .1002914    1.262444
     catdum4 |  -.3197691   .2605409    -1.23   0.220    -.8304199    .1908816
     catdum5 |  -.5375659   .3484466    -1.54   0.123    -1.220509    .1453768
       _cons |  -.7642244    .979824    -0.78   0.435    -2.684644    1.156195
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   .9772476   .2538145       .587391    1.625855
------------------------------------------------------------------------------
LR test vs. logistic regression: chibar2(01) =    19.79 Prob>=chibar2 = 0.0000

. xtmelogit vote retdiff npartdiff partdiff retention npart djudge ijudge closepl closepc catdu
> m2 catdum3 catdum4 catdum5 || stateid: 

Refining starting values: 

Iteration 0:   log likelihood = -796.81541  
Iteration 1:   log likelihood = -791.11442  
Iteration 2:   log likelihood = -788.84495  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -788.84495  
Iteration 1:   log likelihood =  -788.7016  
Iteration 2:   log likelihood = -788.70093  
Iteration 3:   log likelihood = -788.70093  

Mixed-effects logistic regression               Number of obs      =      1233
Group variable: stateid                         Number of groups   =        30

                                                Obs per group: min =         5
                                                               avg =      41.1
                                                               max =       196

Integration points =   7                        Wald chi2(13)      =     34.50
Log likelihood = -788.70093                     Prob > chi2        =    0.0010

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   3.264039   1.449106     2.25   0.024     .4238442    6.104235
   npartdiff |   4.084579   1.532045     2.67   0.008     1.081826    7.087333
    partdiff |   1.378802   1.984323     0.69   0.487      -2.5104    5.268004
   retention |   .6586192   .8499773     0.77   0.438    -1.007306    2.324544
       npart |   .0339633   .8695979     0.04   0.969    -1.670417    1.738344
      djudge |  -.4105371   .1523654    -2.69   0.007    -.7091678   -.1119065
      ijudge |  -.3596128   .4739333    -0.76   0.448    -1.288505    .5692793
     closepl |   .1958448   .1422112     1.38   0.168    -.0828841    .4745736
     closepc |   .5080438   .4899369     1.04   0.300    -.4522149    1.468302
     catdum2 |   .7094344   .2410567     2.94   0.003      .236972    1.181897
     catdum3 |   .3993128   .2449771     1.63   0.103    -.0808335    .8794591
     catdum4 |   .2613831   .2116489     1.23   0.217    -.1534411    .6762072
     catdum5 |  -.0691649   .2541693    -0.27   0.786    -.5673275    .4289978
       _cons |  -1.187657   .8327877    -1.43   0.154    -2.819891    .4445766
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   .8656996   .1800838      .5758366    1.301473
------------------------------------------------------------------------------
LR test vs. logistic regression: chibar2(01) =    44.95 Prob>=chibar2 = 0.0000

. * now a basic logit; just for discussion NO TABLE
. xtlogit vote retdiff npartdiff partdiff retention npart djudge ijudge closepl closepc factpl 
> catdum3 catdum4 catdum5

Fitting comparison model:

Iteration 0:   log likelihood = -561.34814  
Iteration 1:   log likelihood = -534.40286  
Iteration 2:   log likelihood =  -534.3185  
Iteration 3:   log likelihood = -534.31848  

Fitting full model:

tau =  0.0     log likelihood = -534.31848
tau =  0.1     log likelihood = -530.65787
tau =  0.2     log likelihood = -529.85691
tau =  0.3     log likelihood = -530.54692

Iteration 0:   log likelihood = -529.63344  
Iteration 1:   log likelihood = -525.53609  
Iteration 2:   log likelihood = -524.47407  
Iteration 3:   log likelihood = -524.42339  
Iteration 4:   log likelihood = -524.42328  
Iteration 5:   log likelihood = -524.42328  

Random-effects logistic regression              Number of obs      =       818
Group variable: stateid                         Number of groups   =        27

Random effects u_i ~ Gaussian                   Obs per group: min =         3
                                                               avg =      30.3
                                                               max =       154

                                                Wald chi2(13)      =     41.88
Log likelihood  = -524.42328                    Prob > chi2        =    0.0001

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   3.838177   1.936148     1.98   0.047      .043396    7.632959
   npartdiff |    4.81495   1.940152     2.48   0.013     1.012323    8.617578
    partdiff |   -1.28648   2.290627    -0.56   0.574    -5.776027    3.203067
   retention |   .0692697   1.034361     0.07   0.947    -1.958042    2.096581
       npart |   .0754116   1.032729     0.07   0.942      -1.9487    2.099523
      djudge |  -.4042928   .1886844    -2.14   0.032    -.7741075   -.0344782
      ijudge |   -.577909   .5924074    -0.98   0.329    -1.739006    .5831882
     closepl |   .2849205   .1762734     1.62   0.106     -.060569    .6304099
     closepc |    .956099   .8587103     1.11   0.266    -.7269422     2.63914
      factpl |   .5701547   .1979343     2.88   0.004     .1822107    .9580988
     catdum3 |   .6813674   .2964731     2.30   0.022     .1002909    1.262444
     catdum4 |  -.3197703   .2605411    -1.23   0.220    -.8304216    .1908809
     catdum5 |  -.5375682    .348447    -1.54   0.123    -1.220512    .1453755
       _cons |  -.7642263   .9798263    -0.78   0.435    -2.684651    1.156198
-------------+----------------------------------------------------------------
    /lnsig2u |  -.0460204   .5194521                     -1.064128     .972087
-------------+----------------------------------------------------------------
     sigma_u |   .9772525   .2538179                      .5873914    1.625871
         rho |   .2249816   .0905742                      .0949212    .4455269
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =    19.79 Prob >= chibar2 = 0.000

. test retdiff-partdiff=0

 ( 1)  [vote]retdiff - [vote]partdiff = 0

           chi2(  1) =    2.98
         Prob > chi2 =    0.0845

. test retdiff-npartdiff=0

 ( 1)  [vote]retdiff - [vote]npartdiff = 0

           chi2(  1) =    0.14
         Prob > chi2 =    0.7077

. test npartdiff-partdiff=0

 ( 1)  [vote]npartdiff - [vote]partdiff = 0

           chi2(  1) =    4.40
         Prob > chi2 =    0.0359

. xtlogit vote retdiff npartdiff partdiff retention npart djudge ijudge closepl closepc catdum2
>  catdum3 catdum4 catdum5 

Fitting comparison model:

Iteration 0:   log likelihood = -838.19392  
Iteration 1:   log likelihood = -811.23711  
Iteration 2:   log likelihood =  -811.1741  
Iteration 3:   log likelihood =  -811.1741  

Fitting full model:

tau =  0.0     log likelihood =  -811.1741
tau =  0.1     log likelihood = -798.98291
tau =  0.2     log likelihood = -795.98348
tau =  0.3     log likelihood = -796.16864

Iteration 0:   log likelihood = -795.53642  
Iteration 1:   log likelihood = -789.16355  
Iteration 2:   log likelihood = -788.70407  
Iteration 3:   log likelihood = -788.70091  
Iteration 4:   log likelihood = -788.70091  

Random-effects logistic regression              Number of obs      =      1233
Group variable: stateid                         Number of groups   =        30

Random effects u_i ~ Gaussian                   Obs per group: min =         5
                                                               avg =      41.1
                                                               max =       196

                                                Wald chi2(13)      =     34.50
Log likelihood  = -788.70091                    Prob > chi2        =    0.0010

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   3.264043   1.449107     2.25   0.024     .4238451     6.10424
   npartdiff |   4.084579   1.532045     2.67   0.008     1.081826    7.087332
    partdiff |   1.378806   1.984324     0.69   0.487    -2.510398     5.26801
   retention |   .6586198   .8499784     0.77   0.438    -1.007307    2.324547
       npart |   .0339658   .8695991     0.04   0.969    -1.670417    1.738349
      djudge |   -.410537   .1523654    -2.69   0.007    -.7091676   -.1119063
      ijudge |  -.3596125   .4739332    -0.76   0.448    -1.288505    .5692795
     closepl |   .1958448   .1422112     1.38   0.168     -.082884    .4745737
     closepc |   .5080425   .4899368     1.04   0.300    -.4522161    1.468301
     catdum2 |   .7094349   .2410568     2.94   0.003     .2369723    1.181897
     catdum3 |   .3993131   .2449771     1.63   0.103    -.0808333    .8794594
     catdum4 |   .2613832   .2116489     1.23   0.217    -.1534411    .6762074
     catdum5 |  -.0691648   .2541693    -0.27   0.786    -.5673274    .4289978
       _cons |  -1.187659   .8327883    -1.43   0.154    -2.819894    .4445764
-------------+----------------------------------------------------------------
    /lnsig2u |  -.2884303   .4160427                     -1.103859    .5269984
-------------+----------------------------------------------------------------
     sigma_u |   .8657015   .1800844                      .5758376    1.301476
         rho |   .1855365   .0628693                      .0915623    .3398755
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =    44.95 Prob >= chibar2 = 0.000

. test retdiff-partdiff=0

 ( 1)  [vote]retdiff - [vote]partdiff = 0

           chi2(  1) =    0.61
         Prob > chi2 =    0.4354

. test retdiff-npartdiff=0

 ( 1)  [vote]retdiff - [vote]npartdiff = 0

           chi2(  1) =    0.17
         Prob > chi2 =    0.6793

. test npartdiff-partdiff=0

 ( 1)  [vote]npartdiff - [vote]partdiff = 0

           chi2(  1) =    1.32
         Prob > chi2 =    0.2512

. *Laura Langer's party id:  For Refs table
. xtmelogit vote retdiff npartdiff partdiff retention npart djudgelang ijudgelang closepl close
> pc factpl catdum3 catdum4 catdum5 || stateid: || judgeid: 

Refining starting values: 

Iteration 0:   log likelihood = -542.31589  
Iteration 1:   log likelihood = -532.14846  
Iteration 2:   log likelihood = -530.43573  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -530.43573  
Iteration 1:   log likelihood = -530.39356  
Iteration 2:   log likelihood = -530.39343  
Iteration 3:   log likelihood = -530.39343  

Mixed-effects logistic regression               Number of obs      =       831

--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
        stateid |       27          5       30.8        161           7
       judgeid2 |      300          1        2.8         15           7
--------------------------------------------------------------------------

                                                Wald chi2(13)      =     37.18
Log likelihood = -530.39343                     Prob > chi2        =    0.0004

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   4.043596   1.995428     2.03   0.043     .1326292    7.954562
   npartdiff |   4.424601   1.970546     2.25   0.025     .5624014    8.286801
    partdiff |  -1.162888   2.373044    -0.49   0.624    -5.813969    3.488193
   retention |   .1270569   1.069528     0.12   0.905    -1.969179    2.223293
       npart |   .2216068   1.065511     0.21   0.835    -1.866756    2.309969
  djudgelang |  -.2923339   .1982577    -1.47   0.140    -.6809119    .0962441
  ijudgelang |   .0826341   .8393211     0.10   0.922    -1.562405    1.727673
     closepl |   .2589658   .1858992     1.39   0.164      -.10539    .6233216
     closepc |   1.010395   .9100847     1.11   0.267    -.7733384    2.794128
      factpl |   .5935191   .2019137     2.94   0.003     .1977755    .9892628
     catdum3 |   .7249797   .3100601     2.34   0.019     .1172731    1.332686
     catdum4 |  -.3142454   .2676106    -1.17   0.240    -.8387524    .2102617
     catdum5 |  -.4889705   .3573089    -1.37   0.171    -1.189283    .2113421
       _cons |  -.9912763   1.000094    -0.99   0.322    -2.951425    .9688726
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   .9783619   .2618611      .5789929    1.653202
-----------------------------+------------------------------------------------
judgeid2: Identity           |
                   sd(_cons) |   .5215531   .1488427      .2981121    .9124676
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =    25.18   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. xtmelogit vote retdiff npartdiff partdiff retention npart djudgelang ijudgelang closepl close
> pc catdum2 catdum3 catdum4 catdum5 || stateid: || judgeid: 

Refining starting values: 

Iteration 0:   log likelihood =  -823.5616  
Iteration 1:   log likelihood = -806.10213  
Iteration 2:   log likelihood = -805.10553  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -805.10553  
Iteration 1:   log likelihood = -805.09052  
Iteration 2:   log likelihood = -805.09052  

Mixed-effects logistic regression               Number of obs      =      1264

--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
        stateid |       30          5       42.1        205           7
       judgeid2 |      405          1        3.1         20           7
--------------------------------------------------------------------------

                                                Wald chi2(13)      =     31.94
Log likelihood = -805.09052                     Prob > chi2        =    0.0025

------------------------------------------------------------------------------
        vote |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     retdiff |   3.151659   1.455873     2.16   0.030     .2982001    6.005118
   npartdiff |    4.06212    1.55365     2.61   0.009     1.017022    7.107219
    partdiff |   1.778826   2.015722     0.88   0.378    -2.171916    5.729567
   retention |   .7066715   .8563835     0.83   0.409    -.9718093    2.385152
       npart |   .0848979   .8762104     0.10   0.923    -1.632443    1.802239
  djudgelang |   -.350236   .1525898    -2.30   0.022    -.6493065   -.0511654
  ijudgelang |  -.0934915    .647599    -0.14   0.885    -1.362762    1.175779
     closepl |   .1695511   .1450424     1.17   0.242    -.1147267    .4538289
     closepc |   .4889186   .4994673     0.98   0.328    -.4900194    1.467857
     catdum2 |   .8040998   .2436385     3.30   0.001     .3265771    1.281622
     catdum3 |   .3845875   .2475868     1.55   0.120    -.1006737    .8698487
     catdum4 |   .2679541   .2142677     1.25   0.211    -.1520028    .6879111
     catdum5 |  -.0394085   .2584937    -0.15   0.879    -.5460468    .4672299
       _cons |   -1.29346   .8291655    -1.56   0.119    -2.918595    .3316744
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
stateid: Identity            |
                   sd(_cons) |   .8872906   .1810632      .5947947    1.323624
-----------------------------+------------------------------------------------
judgeid2: Identity           |
                   sd(_cons) |   .3781293   .1326861       .190087    .7521909
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =    52.49   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. *end
. 
. 
end of do-file

. log close
      name:  <unnamed>
       log:  c:\data\jleoccp.log
  log type:  text
 closed on:  17 Dec 2010, 10:24:07
-----------------------------------------------------------------------------------------------
